Development and Usability Testing of a Mobile Application to Monitor Patient-Reported Outcomes after Stress Urinary Incontinence Surgery.

OBJECTIVE: To develop and perform a usability testing of a mobile application (app) with representative users of surgeons and female patients undergoing stress urinary incontinence (SUI) surgery. METHODS: A mobile app was developed with the Medical Device Epidemiology Network (MDEpiNet) High-Performance Integrated Virtual Environment (HIVE) to streamline the collection of patient-reported outcomes following SUI surgery using validated questionnaires. It was designed as a collaborative effort with the Women's Health Initiative (WHI), including patient and surgeon involvement. The app evaluation questions addressed the user's rating on the clarity and length of the questions and the comfort level in using the interface for patients to report post-operative outcomes and surgeons to review them. RESULTS: A total of twenty patients and 5 surgeons tested the mobile app and reported their experience and level of satisfaction. The average patient experience score was 9 out of ten, with ten being the best. Eleven patients (55%) were interested in replacing in-person follow-up visits with the app. The surgeons reported an average user experience score of 8.6 out of ten. Four surgeons (80%) were interested in replacing in-person visits with the app. The combined experience score between all users was 8.9 out of ten. Fifteen out of twenty-five users (60%) showed interest in replacing in-person visits with the app. CONCLUSION: The mobile app for SUI captured important patient-reported outcomes with a high satisfaction reporting from patients and surgeons.

A second generation leishmanization vaccine with a markerless attenuated Leishmania major strain using CRISPR gene editing.

Leishmaniasis is a neglected tropical disease caused by Leishmania protozoa transmitted by infected sand flies. Vaccination through leishmanization with live Leishmania major has been used successfully but is no longer practiced because it resulted in occasional skin lesions. A second generation leishmanization is described here using a CRISPR genome edited L. major strain (LmCen-/-). Notably, LmCen-/- is a genetically engineered centrin gene knock-out mutant strain that is antibiotic resistant marker free and does not have detectable off-target mutations. Mice immunized with LmCen-/- have no visible lesions following challenge with L. major-infected sand flies, while non-immunized animals develop large and progressive lesions with a 2-log fold higher parasite burden. LmCen-/- immunization results in protection and an immune response comparable to leishmanization. LmCen-/- is safe since it is unable to cause disease in immunocompromised mice, induces robust host protection against vector sand fly challenge and because it is marker free, can be advanced to human vaccine trials.

Novel reference genes in colorectal cancer identify a distinct subset of high stage tumors and their associated histologically normal colonic tissues.

BACKGROUND: Reference genes are often interchangeably called housekeeping genes due to 1) the essential cellular functions their proteins provide and 2) their constitutive expression across a range of normal and pathophysiological conditions. However, given the proliferative drive of malignant cells, many reference genes such as beta-actin (ACTB) and glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) which play critical roles in cell membrane organization and glycolysis, may be dysregulated in tumors versus their corresponding normal controls METHODS: Because Next Generation Sequencing (NGS) technology has several advantages over hybridization-based technologies, such as independent detection and quantitation of transcription levels, greater sensitivity, and increased dynamic range, we evaluated colorectal cancers (CRC) and their histologically normal tissue counterparts by NGS to evaluate the expression of 21 "classical" reference genes used as normalization standards for PCR based methods. Seventy-nine paired tissue samples of CRC and their patient matched healthy colonic tissues were subjected to NGS analysis of their mRNAs. RESULTS: We affirmed that 17 out of 21 classical reference genes had upregulated expression in tumors compared to normal colonic epithelial tissue and dramatically so in some cases. Indeed, tumors were distinguished from normal controls in both unsupervised hierarchical clustering analyses (HCA) and principal component analyses (PCA). We then identified 42 novel potential reference genes with minimal coefficients of variation (CV) across 79 CRC tumor pairs. Though largely consistently expressed across tumors and normal control tissues, a subset of high stage tumors (HSTs) as well as some normal tissue samples (HSNs) located adjacent to these HSTs demonstrated dysregulated expression, thus identifying a subset of tumors with a potentially distinct and aggressive biological profile. CONCLUSION: While classical CRC reference genes were found to be differentially expressed between tumors and normal controls, novel reference genes, identified via NGS, were more consistently expressed across malignant and normal colonic tissues. Nonetheless, a subset of HST had profound dysregulation of such genes as did many of the histologically normal tissues adjacent to such HSTs, indicating that the HSTs so distinguished may have unique biological properties and that their histologically normal tissues likely harbor a small population of microscopically undetected but metabolically active tumors.

Pubcast and Genecast: Browsing and Exploring Publications and Associated Curated Content in Biology Through Mobile Devices.

Services such as Facebook, Amazon, and eBay were once solely accessed from stationary computers. These web services are now being used increasingly on mobile devices. We acknowledge this new reality by providing users a way to access publications and a curated cancer mutation database on their mobile device with daily automated updates. AVAILABILITY: http://hive. biochemistry.gwu.edu/tools/HivePubcast.

Challenges, Solutions, and Quality Metrics of Personal Genome Assembly in Advancing Precision Medicine.

Even though each of us shares more than 99% of the DNA sequences in our genome, there are millions of sequence codes or structure in small regions that differ between individuals, giving us different characteristics of appearance or responsiveness to medical treatments. Currently, genetic variants in diseased tissues, such as tumors, are uncovered by exploring the differences between the reference genome and the sequences detected in the diseased tissue. However, the public reference genome was derived with the DNA from multiple individuals. As a result of this, the reference genome is incomplete and may misrepresent the sequence variants of the general population. The more reliable solution is to compare sequences of diseased tissue with its own genome sequence derived from tissue in a normal state. As the price to sequence the human genome has dropped dramatically to around $1000, it shows a promising future of documenting the personal genome for every individual. However, de novo assembly of individual genomes at an affordable cost is still challenging. Thus, till now, only a few human genomes have been fully assembled. In this review, we introduce the history of human genome sequencing and the evolution of sequencing platforms, from Sanger sequencing to emerging "third generation sequencing" technologies. We present the currently available de novo assembly and post-assembly software packages for human genome assembly and their requirements for computational infrastructures. We recommend that a combined hybrid assembly with long and short reads would be a promising way to generate good quality human genome assemblies and specify parameters for the quality assessment of assembly outcomes. We provide a perspective view of the benefit of using personal genomes as references and suggestions for obtaining a quality personal genome. Finally, we discuss the usage of the personal genome in aiding vaccine design and development, monitoring host immune-response, tailoring drug therapy and detecting tumors. We believe the precision medicine would largely benefit from bioinformatics solutions, particularly for personal genome assembly.

Generation and propagation of recombinant mumps viruses exhibiting an additional U residue in the homopolymeric U tract of the F gene-end signal.

As a member of the family paramyxoviridae, subfamily paramyxovirinae, the genome of mumps virus (MuV) is postulated to be polyhexameric in length in order to be able to replicate efficiently. While all natural MuV strains sequenced so far obey to this "rule of six," we describe here the isolation of recombinant MuVs that appeared to contain an additional U residue in the homopolymeric tract of the F gene-end signal, resulting in a genome length of 6n + 1. Sequencing of several plaque-purified viruses from these preparations did not reveal the existence of length-correcting mutations, suggesting that they are violators of the rule of six. Employing high-throughput sequencing technology, we provide evidence that the insertion of an additional U residue is mainly the result of the rescue system used that relies on T7 RNA polymerase. Limited in vitro and in vivo testing of the viruses did not reveal any significant impact of the longer genome on virus replication or virulence, suggesting that the rule of six is not a strict requirement for MuV replication.

Human germline and pan-cancer variomes and their distinct functional profiles.

Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.

Whole genome single-nucleotide variation profile-based phylogenetic tree building methods for analysis of viral, bacterial and human genomes.

UNLABELLED: Next-generation sequencing data can be mapped to a reference genome to identify single-nucleotide polymorphisms/variations (SNPs/SNVs; called SNPs hereafter). In theory, SNPs can be compared across several samples and the differences can be used to create phylogenetic trees depicting relatedness among the samples. However, in practice this is difficult because currently there is no stand-alone tool that takes SNP data directly as input and produces phylogenetic trees. In response to this need, PhyloSNP application was created with two analysis methods 1) a quantitative method that creates the presence/absence matrix which can be directly used to generate phylogenetic trees or creates a tree from a shrunk genome alignment (includes additional bases surrounding the SNP position) and 2) a qualitative method that clusters samples based on the frequency of different bases found at a particular position. The algorithms were used to generate trees from Poliovirus, Burkholderia and human cancer genomics NGS datasets. AVAILABILITY: PhyloSNP is freely available for download at http://hive.biochemistry.gwu.edu/dna.cgi?cmd=phylosnp.

Single-nucleotide variations in cardiac arrhythmias: prospects for genomics and proteomics based biomarker discovery and diagnostics.

Cardiovascular diseases are a large contributor to causes of early death in developed countries. Some of these conditions, such as sudden cardiac death and atrial fibrillation, stem from arrhythmias-a spectrum of conditions with abnormal electrical activity in the heart. Genome-wide association studies can identify single nucleotide variations (SNVs) that may predispose individuals to developing acquired forms of arrhythmias. Through manual curation of published genome-wide association studies, we have collected a comprehensive list of 75 SNVs associated with cardiac arrhythmias. Ten of the SNVs result in amino acid changes and can be used in proteomic-based detection methods. In an effort to identify additional non-synonymous mutations that affect the proteome, we analyzed the post-translational modification S-nitrosylation, which is known to affect cardiac arrhythmias. We identified loss of seven known S-nitrosylation sites due to non-synonymous single nucleotide variations (nsSNVs). For predicted nitrosylation sites we found 1429 proteins where the sites are modified due to nsSNV. Analysis of the predicted S-nitrosylation dataset for over- or under-representation (compared to the complete human proteome) of pathways and functional elements shows significant statistical over-representation of the blood coagulation pathway. Gene Ontology (GO) analysis displays statistically over-represented terms related to muscle contraction, receptor activity, motor activity, cystoskeleton components, and microtubule activity. Through the genomic and proteomic context of SNVs and S-nitrosylation sites presented in this study, researchers can look for variation that can predispose individuals to cardiac arrhythmias. Such attempts to elucidate mechanisms of arrhythmia thereby add yet another useful parameter in predicting susceptibility for cardiac diseases.

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE).

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu.

Non-synonymous variations in cancer and their effects on the human proteome: workflow for NGS data biocuration and proteome-wide analysis of TCGA data.

BACKGROUND: Next-generation sequencing (NGS) technologies have resulted in petabytes of scattered data, decentralized in archives, databases and sometimes in isolated hard-disks which are inaccessible for browsing and analysis. It is expected that curated secondary databases will help organize some of this Big Data thereby allowing users better navigate, search and compute on it. RESULTS: To address the above challenge, we have implemented a NGS biocuration workflow and are analyzing short read sequences and associated metadata from cancer patients to better understand the human variome. Curation of variation and other related information from control (normal tissue) and case (tumor) samples will provide comprehensive background information that can be used in genomic medicine research and application studies. Our approach includes a CloudBioLinux Virtual Machine which is used upstream of an integrated High-performance Integrated Virtual Environment (HIVE) that encapsulates Curated Short Read archive (CSR) and a proteome-wide variation effect analysis tool (SNVDis). As a proof-of-concept, we have curated and analyzed control and case breast cancer datasets from the NCI cancer genomics program - The Cancer Genome Atlas (TCGA). Our efforts include reviewing and recording in CSR available clinical information on patients, mapping of the reads to the reference followed by identification of non-synonymous Single Nucleotide Variations (nsSNVs) and integrating the data with tools that allow analysis of effect nsSNVs on the human proteome. Furthermore, we have also developed a novel phylogenetic analysis algorithm that uses SNV positions and can be used to classify the patient population. The workflow described here lays the foundation for analysis of short read sequence data to identify rare and novel SNVs that are not present in dbSNP and therefore provides a more comprehensive understanding of the human variome. Variation results for single genes as well as the entire study are available from the CSR website (http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr). CONCLUSIONS: Availability of thousands of sequenced samples from patients provides a rich repository of sequence information that can be utilized to identify individual level SNVs and their effect on the human proteome beyond what the dbSNP database provides.

Proteome-wide analysis of nonsynonymous single-nucleotide variations in active sites of human proteins.

An enzyme's active site is essential to normal protein activity such that any disruptions at this site may lead to dysfunction and disease. Nonsynonymous single-nucleotide variations (nsSNVs), which alter the amino acid sequence, are one type of disruption that can alter the active site. When this occurs, it is assumed that enzyme activity will vary because of the criticality of the site to normal protein function. We integrate nsSNV data and active site annotations from curated resources to identify all active-site-impacting nsSNVs in the human genome and search for all pathways observed to be associated with this data set to assess the likely consequences. We find that there are 934 unique nsSNVs that occur at the active sites of 559 proteins. Analysis of the nsSNV data shows an over-representation of arginine and an under-representation of cysteine, phenylalanine and tyrosine when comparing the list of nsSNV-impacted active site residues with the list of all possible proteomic active site residues, implying a potential bias for or against variation of these residues at the active site. Clustering analysis shows an abundance of hydrolases and transferases. Pathway and functional analysis shows several pathways over- or under-represented in the data set, with the most significantly affected pathways involved in carbohydrate metabolism. We provide a table of 32 variation-substrate/product pairs that can be used in targeted metabolomics experiments to assay the effects of specific variations. In addition, we report the significant prevalence of aspartic acid to histidine variation in eight proteins associated with nine diseases including glycogen storage diseases, lacrimo-auriculo-dento-digital syndrome, Parkinson's disease and several cancers.